{"id":"W1985134713","doi":"10.1109/med.2010.5547730","title":"Support Vector Regression for soft sensor design of nonlinear processes","year":2010,"lang":"en","type":"article","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Soft sensor; Support vector machine; Computer science; Nonlinear system; Process (computing); Field (mathematics); Soft computing; Scale (ratio); Machine learning; Control engineering; Data mining; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001429539,0.001015385,0.001099777,0.0005106827,0.0002156924,0.0008361274,0.0006830638,0.001041439,0.001476126],"category_scores_gemma":[0.004541697,0.0005469796,0.000599429,0.0004532401,0.0005676857,0.000735827,0.0006384779,0.001407884,0.0004511506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004948438,"about_ca_system_score_gemma":0.0007236011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001666021,"about_ca_topic_score_gemma":0.001164654,"domain_scores_codex":[0.9992575,0.000293616,0.00004513624,0.0001293944,0.0002245841,0.00004981858],"domain_scores_gemma":[0.9983133,0.001015145,0.0002240343,0.00007938249,0.0003372917,0.00003086542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006863846,0.0000315227,0.0002695032,0.0001274138,0.00003437283,0.00004094155,0.00003523125,0.9435217,0.003812595,0.005670437,0.0003981268,0.04598941],"study_design_scores_gemma":[0.000002986088,0.00001550416,0.00002247324,0.000002904903,0.000001781293,0.000002813309,0.000001584972,0.9983703,0.0004902034,0.0009378059,0.0001492458,0.000002279102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005147053,0.0002377252,0.9938152,0.00008690358,0.00001361878,0.00002076338,0.00001772649,0.0002136101,0.0004474471],"genre_scores_gemma":[0.7617354,0.0008243894,0.2331454,0.0001127519,0.00008540603,0.0003577067,0.0002204381,0.0001140991,0.003404343],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001666021,"threshold_uncertainty_score":0.007560253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0136743747013574,"score_gpt":0.2417260084398556,"score_spread":0.2280516337384982,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}